



'''simple docstring'''




import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import ViTMSNForImageClassification, ViTMSNModel
						from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor







class       _lowerCAmelCase				:


	def __init__(self			, lowercase			, lowercase=13			, lowercase=30			, lowercase=2			, lowercase=3			, lowercase=True			, lowercase=True			, lowercase=32			, lowercase=5			, lowercase=4			, lowercase=37			, lowercase="gelu"			, lowercase=0.1			, lowercase=0.1			, lowercase=10			, lowercase=0.02			, lowercase=None			, ):
				A_      :		Any      =	parent
				A_      :		Union[str, Any]      =	batch_size
				A_      :		Optional[int]      =	image_size
				A_      :		str      =	patch_size
				A_      :		Optional[Any]      =	num_channels
				A_      :		Dict      =	is_training
				A_      :		Dict      =	use_labels
				A_      :		Tuple      =	hidden_size
				A_      :		Dict      =	num_hidden_layers
				A_      :		Union[str, Any]      =	num_attention_heads
				A_      :		Dict      =	intermediate_size
				A_      :		List[Any]      =	hidden_act
				A_      :		List[str]      =	hidden_dropout_prob
				A_      :		List[str]      =	attention_probs_dropout_prob
				A_      :		Tuple      =	type_sequence_label_size
				A_      :		Optional[int]      =	initializer_range
				A_      :		Union[str, Any]      =	scope

				# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
				A_      :		str      =	(image_size // patch_size) ** 2
				A_      :		int      =	num_patches + 1


	def        _a      (self				):
				A_      :		Any      =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

				A_      :		int      =	None
				if self.use_labels:
							A_      :		Tuple      =	ids_tensor([self.batch_size]			, self.type_sequence_label_size				)

				A_      :		Optional[int]      =	self.get_config()

				return config, pixel_values, labels


	def        _a      (self				):
				return ViTMSNConfig(
				    image_size=self.image_size			, patch_size=self.patch_size			, num_channels=self.num_channels			, hidden_size=self.hidden_size			, num_hidden_layers=self.num_hidden_layers			, num_attention_heads=self.num_attention_heads			, intermediate_size=self.intermediate_size			, hidden_act=self.hidden_act			, hidden_dropout_prob=self.hidden_dropout_prob			, attention_probs_dropout_prob=self.attention_probs_dropout_prob			, initializer_range=self.initializer_range			, )


	def        _a      (self			, lowercase			, lowercase			, lowercase				):
				A_      :		int      =	ViTMSNModel(config=__SCREAMING_SNAKE_CASE				)
				model.to(__SCREAMING_SNAKE_CASE				)
				model.eval()
				A_      :		Dict      =	model(__SCREAMING_SNAKE_CASE				)
				self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, self.seq_length, self.hidden_size)				)


	def        _a      (self			, lowercase			, lowercase			, lowercase				):
				A_      :		List[str]      =	self.type_sequence_label_size
				A_      :		List[str]      =	ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE				)
				model.to(__SCREAMING_SNAKE_CASE				)
				model.eval()
				A_      :		List[Any]      =	model(__SCREAMING_SNAKE_CASE			, labels=__SCREAMING_SNAKE_CASE				)
				print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}"""				)
				print("""Labels: {labels}"""				)
				self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.type_sequence_label_size)				)

				# test greyscale images
				A_      :		Optional[Any]      =	1
				A_      :		Optional[int]      =	ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE				)
				model.to(__SCREAMING_SNAKE_CASE				)
				model.eval()

				A_      :		Optional[int]      =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
				A_      :		Tuple      =	model(__SCREAMING_SNAKE_CASE				)
				self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.type_sequence_label_size)				)


	def        _a      (self				):
				A_      :		List[Any]      =	self.prepare_config_and_inputs()
				A_,       A_,       A_      :		Any      =	config_and_inputs
				A_      :		Optional[Any]      =	{"""pixel_values""": pixel_values}
				return config, inputs_dict







@require_torch
class       _lowerCAmelCase				(     __snake_case ,     __snake_case ,     unittest.TestCase					):
	__SCREAMING_SNAKE_CASE  :						str						     =   (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
	__SCREAMING_SNAKE_CASE  :						Dict						     =   (
	    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__SCREAMING_SNAKE_CASE  :						Dict						     =   False
	__SCREAMING_SNAKE_CASE  :						Union[str, Any]						     =   False
	__SCREAMING_SNAKE_CASE  :						Optional[Any]						     =   False
	__SCREAMING_SNAKE_CASE  :						str						     =   False


	def        _a      (self				):
				A_      :		List[Any]      =	ViTMSNModelTester(self				)
				A_      :		str      =	ConfigTester(self			, config_class=__SCREAMING_SNAKE_CASE			, has_text_modality=__SCREAMING_SNAKE_CASE			, hidden_size=37				)


	def        _a      (self				):
				self.config_tester.run_common_tests()


	@unittest.skip(reason="""ViTMSN does not use inputs_embeds"""				)
	def        _a      (self				):
				pass


	def        _a      (self				):
				A_,       A_      :		Optional[int]      =	self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_      :		str      =	model_class(__SCREAMING_SNAKE_CASE				)
							self.assertIsInstance(model.get_input_embeddings()			, (nn.Module)				)
							A_      :		Optional[int]      =	model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE			, nn.Linear				)				)


	def        _a      (self				):
				A_,       A_      :		List[Any]      =	self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_      :		Optional[int]      =	model_class(__SCREAMING_SNAKE_CASE				)
							A_      :		Optional[Any]      =	inspect.signature(model.forward				)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							A_      :		str      =	[*signature.parameters.keys()]

							A_      :		Tuple      =	["""pixel_values"""]
							self.assertListEqual(arg_names[:1]			, __SCREAMING_SNAKE_CASE				)


	def        _a      (self				):
				A_      :		List[str]      =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE				)


	def        _a      (self				):
				A_      :		Optional[int]      =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE				)


	@slow
	def        _a      (self				):
				for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A_      :		Dict      =	ViTMSNModel.from_pretrained(__SCREAMING_SNAKE_CASE				)
							self.assertIsNotNone(__SCREAMING_SNAKE_CASE				)






def       a     (					):
			'''simple docstring'''





			A_      :		int      =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
			return image







@require_torch
@require_vision
class       _lowerCAmelCase				(     unittest.TestCase					):


	@cached_property
	def        _a      (self				):
				return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""				) if is_vision_available() else None


	@slow
	def        _a      (self				):
				torch.manual_seed(2				)
				A_      :		Optional[int]      =	ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""				).to(__SCREAMING_SNAKE_CASE				)

				A_      :		Optional[Any]      =	self.default_image_processor
				A_      :		Optional[Any]      =	prepare_img()
				A_      :		Tuple      =	image_processor(images=__SCREAMING_SNAKE_CASE			, return_tensors="""pt"""				).to(__SCREAMING_SNAKE_CASE				)

				# forward pass
				with torch.no_grad():
							A_      :		Tuple      =	model(**__SCREAMING_SNAKE_CASE				)

				# verify the logits
				A_      :		str      =	torch.Size((1, 1000)				)
				self.assertEqual(outputs.logits.shape			, __SCREAMING_SNAKE_CASE				)

				A_      :		int      =	torch.tensor([-0.08_03, -0.44_54, -0.23_75]				).to(__SCREAMING_SNAKE_CASE				)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]			, __SCREAMING_SNAKE_CASE			, atol=1E-4				)				)







"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						float						,							_lowerCamelCase			:						float					) ->							float:

  if density <= 0:
    raise ValueError('Impossible fluid density'					)
  if bulk_modulus <= 0:
    raise ValueError('Impossible bulk modulus'					)

  return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
  import doctest

  doctest.testmod()




